Abstract

In real-time multi-agent navigation, agents need to move towards their goal positions while adapting their paths to avoid potential collisions with other agents and static obstacles. Existing methods compute motions that are optimal locally but do not account for the motions of the other agents, producing inefficient global motions especially when many agents move in a crowded space. In my thesis work, each agent has only a limited sensing range and uses online action selection techniques to dynamically adapt its motion to the local conditions. Experimental results obtained in simulation under different conditions show that the agents reach their destinations faster and use motions that minimize their overall energy consumption.